About the job
Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. We are seeking a talented Applied Scientist to join our advanced robotics team, focusing on developing and applying cutting-edge simulation methodologies for advanced robotics systems. This role centers on research and development of physics-based simulation techniques, sim-to-real transfer methods, and machine learning approaches that enable rapid development, testing, and validation of robotic systems operating in complex, real-world environments.
Responsibilities
Advance physics-based simulation fidelity for contact-rich manipulation and locomotion
Design and build high-performance simulation tools integrated into a robotics design stack
Translate research ideas into robust, verifiable data
Develop methods to quantify and reduce simulation-to-reality gaps across design, safety, and control
Architect scalable simulation solutions for rigid and deformable body dynamics
Build simulation pipelines optimized for a digital twin level of fidelity
Establish frameworks for continuous simulation improvement using real-world hardware
Collaborate with engineering, science, and safety teams on simulation requirements and validation
Qualifications
Minimum
Currently has, or is in the process of obtaining, a PhD in computer science, computer engineering, or related field
2+ years of science, technology, engineering or related field experience
Deep expertise in physics-based simulation, including rigid and deformable dynamics, contact mechanics, computational geometry, and numerical methods
Experience designing and optimizing physics-based simulation systems for high-performance and large-scale computing environments
Strong programming skills in C++ and Python, with an emphasis on maintainable, performance-critical code
Working knowledge of modern physics engines such as MuJoCo, Isaac Lab, Drake, and Newton
Preferred
Experience with reinforcement learning and policy training in simulation
Familiarity with differentiable physics, learned simulation models, or neural physics engines
Background in contact-rich manipulation or legged locomotion simulation
Experience with robotics model formats and pipelines (e.g., URDF, SDF, USD)
Expertise in GPU-accelerated computing and algorithms
Experience deploying simulation-trained policies on real robotic systems
Demonstrated research leadership, from project conception through publication and deployment